Building Structural Health: Advanced Technologies and Applications in Monitoring and Evaluation

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2453

Special Issue Editors


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Guest Editor
School of Civil, Environmental & Mining Engineering, The University of Adelaide, Adelaide, SA 5005, Australia
Interests: nondestructive testing; structural health monitoring; civil structures; ultrasonic; vibration

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Guest Editor
School of Civil, Environmental & Mining Engineering Office, The University of Adelaide, Adelaide, SA 5005, Australia
Interests: guided wave non-destructive evaluation; structural dynamics; structural health monitoring; vibration measurement; system identification; composite materials
Special Issues, Collections and Topics in MDPI journals
Building Material Lab, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo Japan
Interests: non-destructive testing; structural health monitoring
School of Architecture and Civil Engineering, The University of Adelaide, Adelaide, SA 5005, Australia
Interests: structural health monitoring; damage detection; vibration analysis; probabilistic and optimization methods; data-driven methods

Special Issue Information

Dear Colleagues,

This Special Issue, ‘Building Structural Health’, provides a dedicated platform for disseminating cutting-edge research in structural health monitoring (SHM), nondestructive evaluation (NDE), and smart diagnostic technologies for civil, architectural, and infrastructure systems. Contributions from both academic and industry communities are welcomed.

Topics of interest include, but are not limited to, the following:

- Advanced sensing technologies for SHM;
- NDE methods and field applications;
- Data-driven diagnostics using machine learning, deep learning, and statistical models;
- Digital twins and cyber–physical systems for structural simulation and prediction;
- Damage detection, localization, and quantification techniques;
- Signal processing and feature extraction in time–frequency–wavenumber domains;
- Smart materials and embedded sensors for self-sensing capabilities;
- Integration of SHM with building information modeling (BIM);
- Multiscale modeling and performance-based evaluation;
- Structural behavior under multi-hazard scenarios (e.g., earthquake, wind, fire, or corrosion);
- SHM applications to concrete, steel, composite, and masonry structures;
- Real-time monitoring systems for additive and modular construction;
- Life-cycle assessment and decision-making tools for maintenance and resilience.

More examples of Special Issues of Buildings at: https://www.mdpi.com/journal/buildings/special_issues

Dr. Tingyuan Yin
Prof. Dr. Ching-Tai Ng
Dr. Zheng Xu
Dr. Zijie Zeng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • nondestructive testing
  • structural health monitoring
  • civil engineering
  • smart sensors
  • damage detection
  • machine learning
  • signal processing

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Published Papers (2 papers)

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Research

23 pages, 2180 KB  
Article
Quality Risk Management in the Construction of Offshore Wind Farm Jackets: Identification, Evaluation, and Mitigation Strategies
by Wenshan Wang, Ruolin Ruan and Yiqing Yu
Buildings 2026, 16(6), 1129; https://doi.org/10.3390/buildings16061129 - 12 Mar 2026
Viewed by 394
Abstract
With the rapid development of the offshore wind power industry, the construction process of offshore wind power jackets faces numerous quality risks, particularly in welding, coating, and assembly operations. This paper aims to investigate the identification, assessment, and management of quality risks during [...] Read more.
With the rapid development of the offshore wind power industry, the construction process of offshore wind power jackets faces numerous quality risks, particularly in welding, coating, and assembly operations. This paper aims to investigate the identification, assessment, and management of quality risks during the construction of offshore wind turbine foundation structures. By establishing a multidimensional quality risk assessment framework, key risk factors affecting quality were identified through expert interviews and brainstorming sessions. Comprehensive evaluations of these risk factors were conducted using the Entropy Weight Method (EWM), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Grey Relational Analysis (GRA). The findings indicate that welding and coating processes pose the highest risks during construction. Based on these assessments, corresponding risk mitigation measures are proposed, including process optimization, automation enhancement, environmental control, and management system refinement. This study provides theoretical foundations and practical guidance for improving construction quality and reducing costs in offshore wind turbine foundation manufacturing. It advances quality risk management by introducing an integrated evaluation model that addresses the limitations of single-method approaches in complex construction scenarios. Full article
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31 pages, 6548 KB  
Article
Scalable IoT-Based Structural Health Monitoring System for Post-Earthquake Rapid Assessment
by Volkan Ergen and Abdullah Can Zülfikar
Buildings 2026, 16(5), 950; https://doi.org/10.3390/buildings16050950 - 28 Feb 2026
Cited by 1 | Viewed by 1389
Abstract
Rapid and accurate building assessment after an earthquake remains a persistent challenge for engineers in seismic areas. Manual inspections are often slow, hampered by road blockages, damaged utilities, and ongoing aftershock risks. This study presents the design, field deployment, and validation of a [...] Read more.
Rapid and accurate building assessment after an earthquake remains a persistent challenge for engineers in seismic areas. Manual inspections are often slow, hampered by road blockages, damaged utilities, and ongoing aftershock risks. This study presents the design, field deployment, and validation of a scalable IoT-based structural health monitoring (SHM) platform developed for real-time post-earthquake decision support. The system integrates multi-axis MEMS accelerometers and inclinometers, supported by on-site signal processing and a cloud-based analytics backend. A comprehensive damage assessment algorithm evaluates parameters such as frequency changes, inter-storey drift, roof displacement, torsional irregularities, and permanent tilt by combining multiple indicators rather than relying on a single measure. The system was deployed in a 22-storey reinforced concrete office building and continuously recorded several seismic events, including a Mw 6.2 earthquake. The results showed that drift values remained within code-defined limits and no permanent deformation occurred. Event-driven edge processing and optimized data management confirmed the system’s scalability for large building portfolios. The findings indicate that IoT-based SHM platforms can complement conventional inspections by providing rapid, data-driven screening to support resilient urban recovery. Full article
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